Optimization of Actuarial Neural Networks with Response Surface Methodology

TL;DR

This paper applies response surface methodology to optimize hyperparameters of combined actuarial neural networks, improving efficiency and accuracy in mortality forecasting and pricing tasks in actuarial science.

Contribution

It introduces the use of RSM for hyperparameter tuning in CANN, reducing computational effort while maintaining high predictive performance.

Findings

RSM outperforms grid search in hyperparameter exploration

Reduced hyperparameter tuning runs from 288 to 188

Achieved near-optimal out-of-sample Poisson deviance loss

Abstract

In the data-driven world of actuarial science, machine learning (ML) plays a crucial role in predictive modeling, enhancing risk assessment and pricing strategies. Neural networks, specifically combined actuarial neural networks (CANN), are vital for tasks such as mortality forecasting and pricing. However, optimizing hyperparameters (e.g., learning rates, layers) is essential for resource efficiency. This study utilizes a factorial design and response surface methodology (RSM) to optimize CANN performance. RSM effectively explores the hyperparameter space and captures potential curvature, outperforming traditional grid search. Our results show accurate performance predictions, identifying critical hyperparameters. By dropping statistically insignificant hyperparameters, we reduced runs from 288 to 188, with negligible loss in accuracy, achieving near-optimal out-of-sample Poisson deviance loss.